PROJECT SUMMARY The objective of this project is to investigate the causal effect of sociodemographic, clinical, and genetic factors on patient safety outcomes in individuals on high-risk immunosuppressive medications. Patient safety events are a leading cause of morbidity and are a major health care quality problem, causing tens of thousands of deaths each year in the U.S. Although patient safety problems in the U.S. health care system are widely recognized, research to develop new approaches to improve safety is urgently needed. For example, individuals requiring immunosuppressive medications for high-risk conditions may face serious patient safety risks, including prescribing errors, monitoring errors, and preventable adverse events (AEs). However, research regarding which patients may be at a higher risk of experiencing an AE due to medication has not been extensively studied. The overall hypothesis of this project is that sociodemographic, clinical, and genetic factors will demonstrate a causal effect on patient safety outcomes in individuals prescribed high-risk immunosuppressive medications. This project will utilize established electronic health record (EHR)-enabled registries to examine over 35,000 individuals prescribed high-risk immunosuppressive medications from the University of California, San Francisco (UCSF, n=~23,000) and San Francisco General Hospital (SFGH, n=~12,000) to address three related hypothesis. First, factors such as socioeconomic status, race/ethnicity, number of medical comorbidities and preferred language status will demonstrate a causal effect on process errors or AEs in individuals prescribed high-risk immunosuppressive medications. State-of-the-art causal inference statistical methods, which account for missing data, time-varying confounding and censoring will be utilized to test for the presence of these effects. Second, genetic variants within the major histocompatibility complex (MHC) will demonstrate a causal effect on AEs in individuals prescribed high-risk immunosuppressive medications. Existing genomic information for patients with certain autoimmune diseases (n=~1,750) will be used for this analysis. Third, clustering and network approach analyses will identify significant sociodemographic characteristics, clinical features, and genetic variants associated with subgroups of individuals prescribed high-risk immunosuppressive medications. Using computational approaches, individuals will be classified to identify sociodemographic characteristics, clinical features, and genetic variants associated with adverse outcome risk (e.g., those who experience an AE vs. those who do not). The importance of contributing factors to process errors and AEs in individuals prescribed high-risk immunosuppressive medications will be demonstrated through this research and provide new insight into monitoring and improving safety in the ambulatory setting.